March 24th, 2014 by ()

PhD thesis Regression analysis with imprecise data by Andrea Wiencierz

My PhD thesis deals with the statistical problem of analyzing the
relationship between a response variable and one or more explanatory
variables when these quantities are only imprecisely observed.

Regression methods are some of the most popular and commonly
employed methods of statistical data analysis. Like most statistical
tools, regression methods are usually based on the assumption that
the analyzed data are precise and correct observations of the
variables of interest. In statistical practice, however, often only
incomplete or uncertain information about the data values is
available. In many cases, the incomplete or uncertain information
about the precise values of interest can be expressed by subsets of
the observation space. For example, interval-censored and rounded
data can be represented by intervals. As the representation by
subsets allows considering many different forms of uncertainty about
data within the same framework, this representation is adopted in my
thesis and set-valued observations of real-valued variables are
simply called imprecise data.






The aim of my PhD research was to find a regression method that provides reliable insights about the analyzed relationship, even if the variables are only imprecisely observed.

After a review of different approaches proposed in the literature, in my thesis, I present the likelihood-based approach to regression with imprecisely observed variables that we developed in Cattaneo and Wiencierz (2012) and that is named Likelihood-based Imprecise Regression (LIR). In the LIR framework, the regression problem is formalized as a decision problem whose actions are the possible regression functions, whose states are the considered probability distributions, and whose loss function is usually a characteristic of the residuals’ distribution. The LIR methodology consists in
determining likelihood-based confidence regions for the loss of the regression problem on the basis of imprecise data and in regarding the set of all regression functions that are not strictly dominated as the imprecise result of the regression analysis. The confidence
regions consist of the loss values associated with all probability measures that are (to a chosen degree) plausible in the light of the imprecise data, where the relative plausibility of the considered probability measures is measured by the likelihood function induced by the observations. Given the imprecise decision criteria, the Interval Dominance principle is applied to identify the set-valued result. Hence, a LIR analysis usually yields an imprecise result, which can be interpreted as a confidence set for the unknown regression function.

From the general LIR methodology, a robust regression method was derived in Cattaneo and Wiencierz (2012), where quantiles of the residuals’ distribution are considered as loss. Furthermore, an exact algorithm to implement this regression method for the special case of simple linear regression with interval data was developed in Cattaneo and Wiencierz (2013) and implemented in an R package (Wiencierz, 2012). In my thesis, I present and discuss these in detail. Moreover, I study several statistical properties of the robust LIR method. It turns out that this LIR method is robust in terms of a high breakdown point and that it yields highly reliable results in the sense that the coverage probability of the resulting set of regression functions seems to be generally rather high.

In addition to the robust LIR method, I also investigate an alternative approach that was proposed in Utkin and Coolen (2011). This approach generalizes Support Vector Regression (SVR) to situations where the response variable is imprecisely observed. It
consists in applying a Minimin or a Minimax rule to the imprecise maximum likelihood estimates of the loss values associated with the regression functions, and in both cases yields a precise regression estimate. The set-valued decision criteria here consist of the loss values associated with all marginal probability distributions of the unobserved precise data that are compatible with the empirical distribution of the imprecise data. After discussing this approach in detail, I develop an alternative adaptation of SVR to this
situation by following the LIR approach, which further generalizes these methods. In contrast to the Minimin and Minimax methods, the LIR method for SVR usually yields a set-valued result, which appears to be more appropriate when dealing with imprecise data. Moreover, the LIR framework has the advantage that it allows reflecting the data imprecision and accounting for statistical uncertainty at the same time, since both types of uncertainty are expressed by the extent of the set-valued result of the regression analysis.

Finally, I apply the different regression methods to two practical data sets from the contexts of social sciences and winemaking, respectively. In both cases, the LIR analyses provide very cautious inferences.


  • M. Cattaneo and A. Wiencierz (2012). Likelihood-based Imprecise Regression. International Journal of Approximate Reasoning 53, 1137-1154.
  • M. Cattaneo and A. Wiencierz (2013). On the implementation of LIR: the case of simple linear regression with interval data. Computational Statistics, in press.
  • L. V. Utkin and F. P. A. Coolen (2011). Interval-valued Regression and Classification Models in the Framework of Machine Learning. Proceedings of the 7th International Symposium on Imprecise Probability: Theories and Applications. SIPTA. pp. 371-380.
  • A. Wiencierz (2012). linLIR: linear Likelihood-based Imprecise Regression. R-package.

About the author:


Andrea Wiencierz works in the Working Group Methodological Foundations of Statistics and their Applications at the Department of Statistics of the LMU Munich, Germany, where she defended her PhD thesis on December 13, 2013.

February 6th, 2014 by ()

Software for credal classification- by Giorgio Corani

Software for credal classification

A classifier is a statistical model of the relationship between the attributes (features) of an object and its category (class). Classifiers are learned from a training set and later are used on the test set to predict the class of a new object given its features.  Credal classifiers extend traditional classifiers allowing for set-valued (or indeterminate) predictions of classes.  The output set is typically larger when the data set is small or it contains many missing values. Credal classifiers aim at producing reliable classifications also in conditions of poor information.

I am aware of only two software suitable for credal classification.


The first is the JNCC2, authored by myself together with M. Zaffalon. It runs from the command line and it is implemented in Java. The code is open source. It reads the data from an ARFF file; this is the open format developed for WEKA (one of the most important open source software for data mining).

The  downloadable zip file contains the source code and the user manual, with worked examples. Once a data set is provided, the software performs cross-validation, comparing Naive Bayes and Naive Credal Classifier. Continuous features are discretized using (Fayyad and Irani, 1993) algorithm.

JNCC2 enables the conservative treatment of missing data. One can declare whether each attribute of the classification problem is subject to a MAR (missing at random) or to a non-MAR missingness process. See here for an introductory discussion of MAR and non-MAR missing data; here for a practical example of non-MAR data; here for a theoretical discussion on how to perform conservative inference in presence of non-MAR missing data.

JNCC2 reports the most traditional indicators of performance for credal classification; for instance it measures the accuracy of Naive Bayes on the instances on which Naive Credal Classifier is determinate or indeterminate. It also computes other typical indicators of performance, such as the percentage of indeterminate classifications, the number of classes returned on average when indeterminate and so on. The results are written to a text file. The software has been published on the JMLR special track on open source software.


The weka-IP plugin has been developed when preparing the classification chapter of the book Introduction to Imprecise Probabilities. I had the opportunity to spend some time in Granada with J. Abellan , A. Masegosa, S. Moral. Their research group has a large experience in credal classification based on decision trees.

We thus decided to make available a number of credal classifiers under the WEKA interface. Andres  linked the code of  WEKA with that of credal decision trees and JNCC2. He is the maintainer of the package.

Weka-IP contains the following credal classifiers:

  • credal decision trees (paper);
  • naïve credal classifier (paper);
  • lazy naïve credal classifier (paper);
  • credal model averaging (paper).

The last two algorithms had been developed by me by extending the JNCC2 code-base, but I had not previously released the code.

The credal classifiers are available in a separate folds of classifiers, which is not present in the standard WEKA interface.

Weka-IP computes also the utility-based metrics for scoring credal classifiers; in this respect it is more up-to-date compared to JNCC2.

I recommend using the Experimenter interface (manual) of Weka-IP. The Experimenter allows comparing via statistical tests the performance of different credal classifiers, based on the results of cross-validation. Moreover, it allows running several credal classifiers on multiple data sets.

Installation and usage details are discussed in the user manual. Some details require some attention. Credal decision tree require removing missing data, which is done by the filter E_FilteredClassifier. Credal classifiers derived from JNCC2 requires the feature to be discrete, which is done by the filter E_Discretize.

Due to interface reasons, it is not possible to deal with non-MAR missing data.

Another advantage of Weka-IP is that one can exploit the powerful Weka functionalities for data pre-processing, such as feature selection.


The Weka-IP allows to easily get in touch with different credal classifiers, to run them from a graphical user interface and to statistically compare their performances. The code might be regarded as preliminary in different respects (see my previous comment on the need for filters), but its interface is generally very easy to use. If you plan to develop your own new algorithm for credal classification, it might be a good idea to try to add it to the Weka-IP package. In this way, you could quickly compare your new algorithm with previously existing ones.

About the author

G. Corani is senior researcher at the Imprecise Probability Group of IDSIA(Lugano, Switzerland). He obtained his PhD in Information Technology from the Politecnico di Milano in 2005. His research interests are mainly data mining and applied statistic. Most of his work done with imprecise probability regards credal classification. He is author of about 50 international publications.

January 9th, 2014 by ()

Denis D. Mauá’s PhD Thesis on Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks

The Three Dots

My PhD thesis is about connecting three hard computational problems that arise in tasks involving graph-based probabilistic reasoning, namely, the problems of

Roughly speaking, in the MAP inference problem we seek the most
probable explanation of a complex phenomena represented as a Bayesian
network, a graph-based description of a multivariate joint probability
distribution where nodes are identified with random variables and local
conditional probability distributions. By extending a Bayesian network
with actions and utilities, we get an influence diagram. The problem of
planning with influence diagrams is to select a set of actions that
maximizes expected utility. A (strong) credal network is a Bayesian
network whose numerical parameters (the local conditional probability
distributions) have been imprecisely specified as convex sets (instead
point estimates). Belief updating in (strong) credal networks is the
task of computing tight upper and lower bounds on the (conditional)
probability of a certain event, and is largely equivalent to the problem
of assessing the sensitivity of a probabilistic inference in a Bayesian
network to global changes in the model parameters. The similarities and
differences among these three problems are more easily captured by the
following simple example in fault analysis.

Probabilistic Fault Analysis

Consider the simple electric circuit below, in which a light bulb is
powered by a battery according to the state of four switches.

electric circuit
Suppose we are interested in estimating the probable causes of the
light being off. By inspecting the circuit, we see that such an event
could have been caused by a burned light bulb, a depleted battery, or
simply because there is no power at the light bulb socket (among other
causes that we ignore). The last event could have been caused by an
interruption of the energy flow on the left or right trails; the trail
on the left is interrupted only if both switches 1 and 2 are off. The
right trail is interrupted if any of the switches 3 and 4 is off. Such a
collection of intercausal reasonings can be graphically represented by
the acyclic directed graph below.

graph-based causal reasoning
If we assign to each node in that graph a conditional probability
distribution of the corresponding event given its parent events (i.e.,
its immediate predecessors in the graph), the result is a fully
specified Bayesian network. If instead, we associate a closed and convex
set of probability distributions to each node and each configuration of
its parents, we obtain a (separately specified) credal network. To get
an influence diagram, we extend the Bayesian network with actions (for
example, possible fixes or information gathering routines) and utilities
(e.g., the costs of replacing the light bulb or the battery, or the loss
caused by having no light), as in the figure below.

influence diagram
Connecting the dots

At first sight, these three problems seem connected only by means of
their combinatorial or optimization nature, and by the use of graphs as
a concise yet intuitive representation of complex phenomena. Nevertheless, correspondences between instances of these problems have long been noticed in the literature. For instance, it was previously known that belief updating in strong credal networks can be reduced to MAP inference in Bayesian networks [Cano, Cano & Moral, 1994] and vice-versa [De Campos & Cozman, 2005]. De Campos & Ji [2008] showed that planning with influence diagrams can be reduced to belief updating in credal networks, while the converse was proved by Antonucci & Zaffalon [2008].

In my thesis, and jointly with Cassio de Campos and Marco Zaffalon,
we proved the last two missing correspondences, namely, that MAP
inference and planning with influence diagrams can be reduced to one
another. We now know that these three problems are strongly tied by
their computational equivalences. These equivalences increase the
algorithmic toolset available to each problem with the algorithms
developed for the other problems, and allow us to derive bounds on the
computational hardness of each problem. Moreover, they provide an
interesting view of each problem instance by the perspective of the
other corresponding problem instances. For example, Cano, Cano &
Moral [1994] reduced belief updating in strong credal networks to MAP
problem instances in order to use available algorithms for the
latter. In a similar fashion, De Campos & Ji [2008] reduced planning
in influence diagrams to belief updating in credal networks so that the
former problem could be solved using algorithms designed for the
latter. Antonucci & Zaffalon [2008] reduced belief updating in
credal networks to planning in influence diagrams in order to provide a
decision-theoretic view of credal networks. De Campos & Cozman
[2005] showed NP-hardness of belief updating in strong credal networks by a reduction from MAP inference. More recently, we were able to show that a
certain class of planning problems can be solved in polynomial time by
reducing them into instances of credal belief updating that are known to
be polynomial-time computable. Using the converse reduction, we were
able to prove the NP-hardness of structurally very simple instances of
credal belief updating. Notably, these correspondences allowed us to
extend De Campos’ proof of polynomial-time approximability of MAP
inference in Bayesian networks of bounded treewidth and variable
cardinality to planning in influence diagrams and belief updating in
strong credal networks. These are the first results concerning the
approximability of these problems. On the more practical side, we were
able to develop a unified algorithmic framework that approximately
solves any of the problems in polynomial time when both treewidth and
variable cardinality are small.


In summary, the main contributions of my work are:

  • a state-of-the-art anytime algorithm for MAP inference;
  • a state-of-the-art exact algorithm for strategy selection in influence diagrams;
  • a proof of NP-hardness of strategy selection in polytree-shaped influence diagrams of bounded treewidth, even in the approximate case;
  • an FPTAS for the strategy selection problem on diagrams of bounded
    treewidth and bounded cardinality;
  • a proof of NP-hardness of strategy selection even in polytree-shaped diagrams with only binary variables, and a result of tractability of the case in which there is a single value node;
  • a proof of NP-hardness of approximate belief updating in credal networks of bounded treewidth (and unbounded variable cardinality);
  • an FPTAS for belief updating in strong credal networks of bounded treewidth and variable cardinality.

About the author

Denis D. Mauá is currently a Post-Doctoral Researcher at the Decision Making Lab of the University of São Paulo. He obtained his PhD in Informatics from the University of Lugano (Università della Svizzera italiana) in Sep., 2013. From Sep., 2009 and Dec., 2013 he was a Research Fellow at IDSIA’s Imprecise Probability Group.


January 7th, 2014 by ()

Alessandro Antonucci reports on WPMSIIP’2013

The sixth edition of WPMSIIP, the Workshop on Principles and Methods of Statistical Inference with Interval Probability was held in Lugano (Switzerland) between the first and the second week of September 2013. The workshop was a follow-up to previous editions, held in Durham (2008, 2010), Munich (2009, 2012), and Lublijana (2011).

The 2013 edition was organized by the Imprecise Probability Group of IDSIA. About 25 participants from ten different countries attended the workshop. Since its first edition, WPMSIIP is intended as an open forum for researchers interested in interval (and imprecise, thus of great interest for the SIPTA community) probability. Almost all the participants took actively part to the workshop by presenting in their talks ongoing research topics and/or open challenges. Each talk was followed by an open discussion, with no strict time constraints. Yet, despite some very long and intense discussions, the original program was (of course in an imprecise way!) met.

During the first day, the discussion has been focused on classification and regression. Giorgio Corani chaired the classification part: it clearly emerged that the focus of the research on this topic is slightly moving from traditional credal classification to more general (and challenging) data-mining problems like preference learning and multilabel classification. The regression part was chaired by Andrea Wiencierz and the importance of new regression tools to cope with interval data was one of the main output of the discussion. The second day, chaired by Alessio Benavoli and Marco Cattaneo, covered different topics related to learning. An increasing interest for filtering based on interval/imprecise methods was observed, together with the need of novel learning tools for non-parametric imprecise models. Inference was the topic of the third day. Cassio Polpo de Campos chaired the discussion, which was mostly specialized to imprecise probabilistic graphical models. Two major topics were discussed: credal networks with epistemic irrelevance and the application to logic of imprecise models. Decision making and evaluation problems were discussed on the fourth day, chaired by Denis Mauá. The utility-based approach seems a satisfactory answer to the evaluation of imprecise classifiers. The situation is definitely more open for decision making. Finally, on the last day, open problems ranging from very theoretical to very research applied topics were discussed.

Despite such a packed program, it was possible to find time for a hiking excursion in the beautiful Swiss Alps.


In summary, the WPMSIIPs meetings should be regarded as an important resource for the SIPTA community. The open format of the workshop allows for exhaustive (and sometimes exhausting!) discussions, something which could hardly be reproduced in other forums. Most of the slides of the talks are available in the workshop website. We look forward to seeing you at WPMSIIP 2014!

September 24th, 2013 by Sebastien Destercke ()

The Eighth International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA ’13)

The Eighth International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA ’13) took place in the nice city of Compiègne, from July 2nd to July 5th. We have to thank Fabio Cozman, Thierry Denoeux, Teddy Seidenfeld (and myself) for the scientific organization, and Cédric Baudrit, Véronique Berger-Cherfaoui, Thierry Denoeux, Mylène Masson, Benjamin Quost, Mohamed Sallak (and myself) for the local organization. With the participants, they all contributed to make of this ISIPTA a very interesting and friendly meeting.

Pursuing ISIPTA tradition, a total of 38 papers were presented at the conference, each of them with a short talk followed by a poster session. Poster-only contributions were given a one-minute presentation. In addition 4 invited presentations were given: by Alessio Benavoli (“Pushing Dynamic Estimation to the Extremes: from the Moon to Imprecise Probability”), Isaac Elishakoff (“Recent Developments in Applied Mechanics with Uncertainties”), Christophe Labreuche (“Robustness in Multi-Criteria Decision Making and its relation with Imprecise Probabilities”) and Jean-Marc Tallon (“Ambiguity and ambiguity attitudes in economics”).

Before the conference, the first of July was dedicated to tutorials, with Matthias Troffaes introducing imprecise probabilities from an historical perspective, while Thierry Denoeux talked about the basics and latest applications of belief functions. A welcome reception was then given at Saint-Nicolas Room.

On Tuesday evening, all participants gathered in the town hall in which the city representatives welcomed the participants. The speech was followed by a drink well-deserved after a first day of hard work.


Thursday had two important events. The first one was the general meeting, in which the new Executive Committee was appointed. The second was the gala dinner at Pierrefond Castle, during which outgoing members (Alessio Benavoli, Frank Coolen and Teddy Seidenfeld) of the Executive Committee were thanked for their hard work for the community, by the means of appropriated gifts (wine, beer, chocolate and books). The dinner was also the occasion to award the winners of the IJAR prize, whose recipients were:

  • Andrea Wiencierz (gold prize)
  • Ignacio Montes Gutiérrez (gold prize)
  • Rocco de Rosa (honorable mention)

The last conference day was Friday, with the closing ceremony ending with the posters awards. The best poster award was given to Erik Quaeghebeur for his poster on “Characterizing Coherence, Correcting Incoherence”. A mention was also given to the posters by:

  • Felipe Aguirre, Christelle Jacob, Sébastien Destercke, Didier Dubois, Mohamed Sallak
  • Arthur Van de Camp, Gert de Cooman
  • Marco Cattaneo
  • Jasper de Bock, Gert de Cooman

We thank all participants, authors, reviewers and organizers for making this ISIPTA a success!

May 17th, 2013 by Erik Quaeghebeur ()

SIPTA & Open Access

Dear colleagues,

On Wednesday 15 May, I participated in the meeting ‘Open access versus Commercial Publishing’ organized by the maths and computer science section of the Dutch Royal Academy of Sciences. The speakers consisted of two clear proponents of Open Access, an Elsevier Senior VP presenting her Company’s efforts in the Open Access area, and somebody giving a more descriptive overview of the issues at hand.

I found the meeting very interesting and I think the topic is relevant to all researchers. I also feel that SIPTA, as a learned society, and its members, as authors, reviewers, and editors, have a role to play.

The basic  issue is that the big publishers, such as Elsevier, Springer, and Wiley, seem to offer a bad deal to researchers and the society at large, which funds the researchers: Journal subscription costs are too high for libraries to pay and are often offered in bundles including many unwanted journals. This results in people not having straightforward access to papers relevant to their research. The posting of papers on personal websites does not provide a structural solution to this problem and is sometimes even prohibited.

The academic world is slow to form a coherent response. Some funding agencies and institutes have started to require that the publications of the researchers they finance or employ be made available under some open access model. But currently the libraries are still struggling. Publishers seem to adapt their business model only at a glacial pace. Grassroots initiatives, usually started because of outrage over publisher behavior, have managed to generate enough pressure to effect more rapid, but still quite limited changes.

So pressuring publishers does help, and this is where I think we as a community and individuals can make a difference:

  • Authors can add Open Access and publishing under a Creative Commons license to the factors that determine where they publish. This is mostly something established researchers with permanent positions have the opportunity to do. Also with big publishers this is now an option, usually costly, but some funding agencies specifically provide support (check with yours!).
  • Reviewers for journals published by entities with absurdly high profit margins are usually not remunerated in any meaningful way for their work. Publishers such as Elsevier are considering options such as making review work count toward paying for their Open Access options. To keep up the pressure to make this a reality, include a cost estimate of your reviewing efforts, e.g., estimated time spent × hourly wage × (1+overhead percentage), with every review for a commercial journal, urging the editor to let the managing editor know you wish this to count toward making a paper of yours Open Access. A more radical option is to refuse to review a paper that, if accepted, is not going to be Open Access.
  • Editors and the editorial board of commercial journals can take a tough stance when negotiating with the publisher, to bring down costs for Open Access options, make copyright transfer a thing of the past, and investigating other options for providing freer access.

While I think commercial publishers will continue to have an important role to play, I also think structurally different alternatives would provide for some healthy competition. For example, SIPTA as a Society could consider creating an arXiv overlay journal.

I hope I have given you some food for thought and look forward to discussion this issue with you during ISIPTA ’13, so I hope to see you there!

Erik Quaeghebeur

October 23rd, 2012 by Erik Quaeghebeur ()

WPMSIIP ’12: Fifth Workshop on Principles and Methods of Statistical Inference with Interval Probability

Gero Walter reports: From September 10th to 15th, the Working Group Methodological Foundations of Statistics and their Applications of the Department of Statistics at LMU Munich hosted the Fifth Workshop on Principles and Methods of Statistical Inference with InteWPMSIIP '12 Group Portraitrval Probability in Munich. During one week, recent research in the field of Imprecise Probability Theory and the potential of imprecise methods to improve statistical analysis was vividly discussed by the workshop’s participants, who included international guests from the UK, France, Russia, Iran, Slovenia, Canada, and Switzerland. An updated programme is available on the workshop’s website.

October 3rd, 2011 by Enrique Miranda ()

A brief report on ISIPTA’2011

The 7th International Symposium on Imprecise Probability: Theories and Applications was held in the beautiful city of Innsbruck (Austria), on July 25—28, 2011. It was organized by Frank Coolen, Gert de Cooman, Thomas Fetz and Michael Oberguggenberger, with the help in the local organization of Anna Bombasaro, Bernhard Schmelzer and Reinhard Stix.Hjemmelaget Oppblåsbare telt

A total of 40 papers dealing with theoretical and practical aspects of imprecise probabilities were presented, both in poster and with a short talk.

Moreover, and continuing with the tradition started at the ISIPTA’09 conference in Durham, poster-only presentations were made of papers presenting novel ideas and applications whose research was not yet completed.

On Monday July 25 the ISIPTA General Meeting was held and the new Executive Committee was appointed.

The continuing president, Teddy Seidenfeld, acknowledged the work of the salient members, and particularly of tian xiao chengthe outgoing secretaryUnited States, Erik Quaeghebeur.

A report on that meeting can be found in

In addition to the presentations, a special session was devoted on Tuesday 26 to Bruno de Finetti, who was born in Innsbruck in 1906, as a commemoration of the eightieth anniversary of the publication of the famous “De Finetti theorem”. This session had the participation of Fulvia de Finetti, Bruno de Finetti’s daughter, who gave a historical account of Prof. de Finetti’s life; buy essays online uk Paolo Vicig and Teddy Seidenfeld, who discussed Bruno de Finetti’s ideas on imprecision; Gert de Cooman, who presented some results extending De Finetti’s work on exchangeability to the imprecise case; and Reinhard Viertl, who discussed both De Finetti’s relationship with Austria and the connection between his work and fuzzy probability distributionsHüpfburgen .

After the session, the ISIPTA’11 participants gathered at de Finetti’s birthplace were a memorial tablet in his honour was unveiled.Monster Park eau gonflable

Wednesday 27 was also the day of the gala dinner, that took place at the Seegrube restaurant, and where the IJAR Young Researcher Awards were given. The recipients were Bernhard Schmelzer (Gold Award), Rebecca Bakeruk cow belly bouncer, Nathan Hunley, Gero Walter, Richard Crossman and David Sundgren (Silver Award) and Mohamed Boujelben, Jasper de Bock and Gerardo Simari (Honorable Mention).

Finally, on the last day of the conference three interesting tutorials on engineering applications of imprecise probabilities were given by Alberto Bernardinibouncy castle for sale canada, Fulvio Tonon and Michael Oberguggenberger. And, last but not least, during the closing ceremony the Best Poster Award was given to the poster by Manuel Eugster, Gero Walter and Thomas Augustin, entitled A Network Analysis of the Imprecise Probability Community based on ISIPTA Electronic Proceedings with a mention to the posters by:

- Jasper de Bock and Gert de Cooman.

- Erik Quaeghebeur, Gert de Cooman and Filip Hermans.

- Arthur Van Camp, Jasper de Bock, Erik Quaeghebeur, Gert de Cooman and Filip Hermans East Inflatable Rentals.

- Fabio Cuzzolin.

We believe that the ISIPTA’11 conference continued to be the success of the previous conferences, and we would like to thank here both the organizers and the participants for having made this possible through their efforts.